1. Field of the Invention
The present invention relates to a method and an apparatus for detecting a presence of an object belonging to a prescribed category, in a region defined over the background.
2. Description of the Background Art
Various types of a service using the image data as the inputs are becoming available as a result of the recent advances in the fields of computer and the communication.
Among such a service, the object detection method for detecting a presence of an object in a prescribed region on a basis of an image of the region is the core technique in the following practical applications.
(1) A remote administration support system in which an open space in the parking area or a hall such as a concert hall is detected for the purpose of guiding the customers.
(2) An automatic monitoring system for detecting the occurrence of an abnormality such as an intruding person or object.
(3) An automatic counting system for counting a number of vehicles, persons, or traffic flows.
Now, as a conventional method for detecting an object in a prescribed region on a basis of an image of the region taken from a fixed camera position, the following two methods have been known.
(1) A method in which the presence of the object is estimated by calculating features such as an edge feature or an edge direction obtained from a brightness of the region, or a shape of the object estimated from such features, and then comparing the similar features obtained in advance for the object to be detected.
(2) A method utilizing a mean brightness of the region, a mean brightness of the partitioned regions, or frequency distribution of direction codes in a differential image.
However, these conventional methods are associated with the following problems.
Namely, in the method (1), it is necessary to have a model for the object to be detected in advance. Here, in a case the object to be detected has a specific invariable shape it is possible to devise such a model, but in a case the object to be detected is defined only in terms of a category it belongs to such as a human being or a car, it is difficult to devise a general model applicable to all the objects belonging to the particular category.
In addition, in this method (1), it is further necessary to have the quantities that can be used in characterizing the object to be detected. For example, the difference values and the correlation coefficients have been used as such quantities for characterizing the object to be detected conventionally. However, when these quantities are used to characterize the object to be detected, a satisfactory detection result could not have been obtained under the adverse condition such as a case of detecting a black object in a dark environment or a case of detecting a white object in a bright environment.
On the other hand, the method (2) has been associated with the problems that there is a tendency for the detection result to be affected by the irregular pattern on the background image, the variation of the overall environmental brightness, and the local variation of the brightness.
Generally speaking, the conventional object detection method has been dissatisfactory in the following three points.
(1) A lack of robustness with respect to the environmental variations. The conventional method has been capable of operating properly only at the specific environmental condition for which it is designed to work, so that the detection result could be severely affected by the environmental variations.
(2) A lack of general applicability. The conventional method has been capable of operating only with respect to the specific object to be detected for which it is designed to work, so that the same method was not readily applicable to the detection of other objects.
(3) A lack of customizability. The conventional method has been designed for a specific setting alone, and could not have been adapted to particularities of the other circumstances in which the method may be operated.
Furthermore, as a conventional method for detecting a state transition in a prescribed region on a basis of an image of the region taken by a fixed camera, there has been a method in which the state transition is detected on a basis of the difference of two sequential images.
However, in this method for detection a state transition, a time interval between the two sequential images for which the difference is to be taken must be quite short in order to detect the state transition accurately.
Moreover, in this method for detecting a state transition, it has been difficult to detect a presence of a particular target object along with the state transition.